Fluorescence (or Forster) resonance energy transfer (FRET), and in particular, energy transfer efficiency (E %), represents a powerful tool to investigate and quantitate biological processes, including protein-protein interactions and co-localization. For energy transfer to take place, four conditions have to be met. First, there has to be significant overlap between the donor fluorophore emission spectra and the acceptor fluorophore excitation spectra. Second, the average distance between donor and acceptor fluorophore molecules should be around 10 to 100 Å. Third, there has to be optical dipole-dipole orientation of donor and acceptor molecules. Fourth, the donor has to exhibit sufficient quantum yield. Since energy transfer itself is a dipole-dipole interaction, no photons are transferred.
There are certain shortcomings of FRET microscopy, which need to be addressed when attempting quantitative approaches. Because of the spectral overlap, necessary for FRET to occur in the first place, the signal is contaminated with donor and acceptor spectral bleed-through (SBT). The overlap between the donor and acceptor emission spectra results in the donor SBT. Acceptor SBT occurs when the donor excitation wavelength excites part of the absorption spectrum of the acceptor. Emission filters with different bandwidths have been used to remove donor crosstalk and acceptor bleed-through contamination, and are useful provided they do not cause a major reduction in the FRET signal.
Numerous algorithm-based FRET correction methodologies exist to remove donor and acceptor SBT, including, for example, those described in the following articles: G. W. Gordon et al., “Quantitative Fluorescence Resonance Energy Transfer Measurements Using Fluorescence Microscopy”, Biophs. J., Vol. 74, pages 2702-2713 (1998); P. S. Bastiaens et al., “Microspectroscopic Imaging Tracks the Intracellular Processing of a Signal Transduction Protein: Fluorescent-Labeled Protein Kinase C Beta I”, Proc. Natl. Acad. Sci., U.S.A., Vol. 93, pages 8407-8412 (1996); F. S. Wouters et al, “FRET Microscopy Demonstrates Molecular Association of Non-Specific Lipid Transfer Protein (nsL-TP) with Fatty Acid Oxidation Enzymes in Peroxisomes”, EMBO J., Vol. 17, pages 71797189 (1998); C. E. Chamberlain, et al., “Imaging Spatiotemporal Dynamics of Rac Activation in vivo with FLAIR”, Methods Enzymol., Vol. 325, pages 389-400 (2000); Z. Xia et al., “Reliable and Global Measurement of Fluorescence Resonance Energy Transfer Using Fluorescence Microscope” Biophys. J., Vol. 81, pages 2395-2402 (2001); and M. Elangovan et al., “Characterization of One- and Two-Photon Fluorescence Resonance Energy Transfer Microscopy”, Methods, Vol. 29, pages 58-73 (2003), all of which are hereby incorporated herein by reference in their entirety.
One advantage of FRET is the ability to employ a variety of imaging systems, making it accessible to many researchers. Depending on imaging needs, one or more different systems may be suitable. Wide-field FRET microscopy might be ideal to investigate the cell nucleus or non-polarized cells; two-photon/multi-photon is best suited for thicker specimens (for example, greater than 100 μm) or donor and acceptor fluorophores with large spectral overlaps, e.g., CFP-YFP; one-photon laser-based scanning or arc lamp-based spinning-disk confocal microscopy systems can be employed for research of polarized cell monolayers that require the acquisition of discrete cellular focal planes at different heights; and investigation at the cell surface might best be done by total internal reflection fluorescence (TIRF).
As noted, one instrument- and biology-related issue in intensity-based FRET quantitative microscopy is spectral bleed-through (SBT) and background correction. Two main components are included in SBT; that is, the donor emission that crosses over into the acceptor emission spectrum (donor SBT) and the acceptor absorption that is excited by the donor excitation wavelength (acceptor SBT). In certain cases, the FRET signal is also contaminated by the acceptor absorption wavelength exciting the donor; however, this “back-bleed-through” is usually extremely low and within the background noise level.
There are a number of methods to address SBT contamination in intensity-based FRET. Each method has its own specific limitations, and the choice depends upon the level of sensitivity desired. For example, the degree of SBT can be established to determine whether SBT is linear, proportional or range dependent. Then, the most appropriate method for SBT correction can be selected, depending on the sensitivity required, the level of the FRET signal, and whether distance estimates are desired. One available approach is entitled “Precision FRET” (PFRET) available through CircuSoft Instrumentation, of Hockessin, Del. PFRET correction is an algorithm-based SBT correction method developed to generate corrected images that represent actual energy-transfer levels (PFRET images). The PFRET algorithm has two components, namely, one component which employs a specific algorithm for pixel-by-pixel SBT correction, which removes donor and acceptor SBT on the basis of matched fluorescence levels between the double-labeled specimen and single-labeled reference specimens, and the other which deals with the quantitative analysis of FRET images. The PFRET SBT correction method used to generate the PFRET images, i.e., PFRET=(uFRET)−(SBT) is actually based on the average value of narrow fluorescence ranges, for more efficient running of the correction algorithm (wherein uFRET is the uncorrected FRET).
The energy transfer efficiency (E %) can be calculated as a percentage of energy transfer in relation to the unquenched donor, as described in an article by H. Wallrabe et al., entitled “Issues in Confocal Microscopy for Quantitative FRET Analysis”, Microscopy Research and Technique, Vol. 60, pages 196-206 (2006).
To analyze FRET data, visual inspection is conventionally employed to select appropriate regions of interest (ROIs) from the corrected FRET image (PFRET), and identify their pixel coordinates. These pixel locations are applied to the other images, and the fluorescence values are extracted (e.g., the acceptor fluorescence intensity image and the donor fluorescence intensity image and uFRET). Acceptor, quenched donor, and PFRET values are averaged over each ROI based on the original pixel-by-pixel analysis and transferred to a spreadsheet for calculation of additional parameters, such as energy transfer efficiency, unquenched donor and acceptor levels, and actual unquenched donor to acceptor ratios.
One drawback to the above approach is that a technician is required to visually inspect the images and manually select appropriate regions of interest. This selection of regions of interest is necessarily subjective, time consuming and expensive. Thus, there is a need in the art for an automated, computer-implemented image processing method and apparatus for selecting regions of interest within an image, such as a corrected FRET image (PFRET), to allow for implementation of an integrated quantitative FRET analysis for, for example, assaying the organization and distribution of receptors such as polymeric IgA receptors and transferrin receptors, as well as other biological applications of FRET.